skip to main content

Real-time currency recognition on video using AKAZE algorithm

Department of Software Engineering, Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia

Received: 3 Nov 2020; Revised: 7 Jul 2021; Accepted: 18 Jul 2021; Available online: 4 Aug 2021; Published: 30 Oct 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because the computation time in processing video data frames is speedy. This study proposes a method that is faster than the SIFT algorithm so that the currency recognition system can run in real-time processing. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of and its speed. Based on the experimental results, the AKAZE algorithm is a resulting value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm is resulting in a value of 0.65 and a speed of 0.305 seconds to process one frame. These results prove that the AKAZE algorithm is faster and more accurate to process video data.
Fulltext Email colleagues
Keywords: currency recognition; SIFT algorithm; AKAZE algorithm; real-time video data
Funding: Institut Teknologi Telkom Purwokerto

Article Metrics:

Last update:

No citation recorded.

Last update: 2022-01-18 22:10:37

No citation recorded.